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All Journal Journal of Marine Research ARABIYAT Jurnal Pengelolaan Sumberdaya Alam dan Lingkungan (Journal of Natural Resources and Environmental Management) Jurnal Informatika dan Teknik Elektro Terapan Jurnal Studi Agama dan Masyarakat Bianglala Informatika : Jurnal Komputer dan Informatika Akademi Bina Sarana Informatika Yogyakarta Madaniyah: Terciptanya Insan Akademis Berkualitas Dan Berakhlak Mulia JIKO (Jurnal Informatika dan Komputer) qolamuna : Jurnal studi islam Jurnal SOLMA BAREKENG: Jurnal Ilmu Matematika dan Terapan JURNAL ILMIAH INFORMATIKA Jurnal Inovasi Hasil Pengabdian Masyarakat (JIPEMAS) QARDHUL HASAN: MEDIA PENGABDIAN KEPADA MASYARAKAT Jambura Journal of Food Technology Jurnal Tekno Kompak Jurnal Ilmiah ILKOMINFO - Ilmu Komputer & Informatika Jurnal Edukasi (Ekonomi, Pendidikan dan Akuntansi) JURSIMA (Jurnal Sistem Informasi dan Manajemen) JATI (Jurnal Mahasiswa Teknik Informatika) Jurnal Biogenerasi BERNAS: Jurnal Pengabdian Kepada Masyarakat Al-Fusha : Arabic Language Education Journal Jurnal Pendidikan dan Teknologi Indonesia Mosharafa: Jurnal Pendidikan Matematika Tanwir Arabiyyah: Arabic as Foreign Language Journal Jurnal EBONI Prosiding Seminar Nasional Sisfotek (Sistem Informasi dan Teknologi Informasi) Journal of Artificial Intelligence and Engineering Applications (JAIEA) Makara Journal of Technology Indonesian Journal of Fundamental Sciences Journal of Tourism Education Prosiding Seminar Nasional Manajemen dan Ekonomi JURSIMA Iqtida: Journal of Da'wah and Communication Huma: Jurnal Sosiologi Nuansa Informatika AMMA : Jurnal Pengabdian Masyarakat Jurnal Biologi Babasal Al-Zayn: Jurnal Ilmu Sosial & Hukum Jurnal Sistem Informasi dan Manajemen Maddana: Jurnal Pengabdian Kepada Masyarakat Sirajuddin : Jurnal Penelitian dan Kajian Pendidikan Islam Prosiding Seminar Nasional Unimus Informasi interaktif : jurnal informatika dan teknologi informasi Jurnal Riset Multidisiplin Edukasi J-CEKI PESHUM Jurnal Geosaintek Sirajuddin: Jurnal Penelitian dan Kajian Pendidikan Islam
Ahmad Faqih
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Using the Apriori Algorithm to Identify Purchase Patterns for Enhancing Sales in Personal Shopper Services Fadilah, Euis; Ahmad Faqih; Sandy Eka Permana
Journal of Artificial Intelligence and Engineering Applications (JAIEA) Vol. 4 No. 2 (2025): February 2025
Publisher : Yayasan Kita Menulis

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.59934/jaiea.v4i2.741

Abstract

This research aims to explore the application of the Apriori algorithm in identifying purchasing patterns in the drop-off service industry in order to increase sales. Drop-off services often face challenges in designing effective marketing strategies due to limited understanding of customer purchasing behavior. In this study, the Apriori algorithm is applied to uncover recurring purchase patterns among customers, which are then used to develop more efficient marketing strategies. Customer transaction data is analyzed to find associations that reflect their purchasing preferences. The results show that the application of the Apriori algorithm successfully identifies patterns that can improve marketing strategies and, ultimately, increase sales. This research emphasizes the importance of applying data mining techniques to improve the performance of delivery services.
The Effect of SMOTE Application on Support Vector Machine Performance in Sentiment Classification on Imbalanced Datasets Andriyani, Dini; Ahmad Faqih; Sandy Eka Permana
Journal of Artificial Intelligence and Engineering Applications (JAIEA) Vol. 4 No. 2 (2025): February 2025
Publisher : Yayasan Kita Menulis

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.59934/jaiea.v4i2.742

Abstract

This research explores the effect of applying Synthetic Minority Oversampling Technique (SMOTE) on the performance of Support Vector Machine (SVM) algorithm in sentiment classification on imbalanced datasets. Public review data was collected from social media platform X (formerly Twitter) regarding the Free Lunch Program, with a total of 2,368 reviews automatically labeled using the BERT model into three categories: positive, negative, and neutral. Sentiment imbalance in the dataset was addressed by applying SMOTE to generate synthetic data on minority classes. The research method follows the stages of Knowledge Discovery in Databases (KDD), including data selection, preprocessing, labeling, transformation using TF-IDF, SVM model training, and performance evaluation. The experimental results show that the application of SMOTE successfully improves the accuracy of the SVM model by 12.48%, from 71.41% to 83.89%. Other evaluation metrics, such as precision, recall, and F1-score, also showed significant improvement from 0.69, 0.71, and 0.68 to 0.84, respectively. These findings confirm that SMOTE is effective in overcoming data imbalance, resulting in a more accurate and reliable sentiment classification model. This research contributes to the application of sentiment analysis in data-driven public policy evaluation.
Improving Sentiment Analysis Performance of Tokopedia Reviews Using Principal Component Analysis and Naïve Bayes Algorithm Lestari, Anjar Ayuning; Ahmad Faqih; Gifthera Dwilestari
Journal of Artificial Intelligence and Engineering Applications (JAIEA) Vol. 4 No. 2 (2025): February 2025
Publisher : Yayasan Kita Menulis

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.59934/jaiea.v4i2.743

Abstract

Tokopedia one of Indonesia's largest e-commerce platforms, offers a wide range of products with diverse customer reviews. These reviews reflect consumer opinions and provide valuable insights for service improvement and marketing strategies. Sentiment analysis is crucial for understanding customer perceptions, but processing large-scale, high-dimensional text data remains a challenge, impacting model efficiency and accuracy. This research uses Principal Component Analysis (PCA) to reduce data dimensionality without losing important information for sentiment classification. The study begins by collecting Tokopedia product reviews and preprocessing the text, including data cleaning, tokenization, stopword removal, and stemming. The reviews are then converted into numerical vectors using the Term Frequency-Inverse Document Frequency (TF-IDF) method. A Gaussian Naïve Bayes model is employed to classify sentiment into three categories: positive, neutral, and negative. The results demonstrate that PCA significantly improves model accuracy from 63.13% to 70.47%, with gains in precision (71.85%), recall (70.47%), and F1-score (71.06%). This research contributes to enhancing sentiment analysis techniques using PCA for Tokopedia reviews and offers a valuable approach that can be applied to other e-commerce platforms.
The Impact of Principal Component Analysis Dimensionality Reduction on Sentiment Classification Performance Using Support Vector Machine Fajria, Azzahra Moudy; Faqih, Ahmad; Dwilestari, Gifthera
Journal of Artificial Intelligence and Engineering Applications (JAIEA) Vol. 4 No. 2 (2025): February 2025
Publisher : Yayasan Kita Menulis

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.59934/jaiea.v4i2.744

Abstract

This study investigates the application of Principal Component Analysis (PCA) to enhance sentiment classification performance using the Support Vector Machine (SVM) algorithm. User reviews of the ChatGPT application from the Play Store were collected, preprocessed, and analyzed to identify the sentiment within the text (positive, negative, or neutral). The research follows the Knowledge Discovery in Databases (KDD) framework, starting with data selection, preprocessing, transformation, and applying PCA for dimensionality reduction. PCA was used to reduce the complexity of the high-dimensional text data, improving SVM's efficiency in sentiment classification. Evaluation results show that applying PCA led to an improvement in model performance, with accuracy increasing from 72.65% to 73.20%, precision from 71.58% to 72.24%, recall from 71.77% to 72.66%, and F1-score from 71.56% to 72.32%. Although the improvements were modest, the findings demonstrate that PCA effectively simplifies complex datasets and enhances SVM performance in sentiment classification, offering benefits in processing high-dimensional text data.
Naïve Bayes Optimization by Implementing Genetic Algorithm in Sentiment Analysis of BCA Mobile Reviews Rizqy, Muhammad Enricco; Faqih, Ahmad; Dwilestari, Gifthera
Journal of Artificial Intelligence and Engineering Applications (JAIEA) Vol. 4 No. 2 (2025): February 2025
Publisher : Yayasan Kita Menulis

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.59934/jaiea.v4i2.750

Abstract

The development of the digital era has encouraged the adoption of mobile banking applications that facilitate banking transactions, including the BCA Mobile application which is simple but still adheres to a slightly outdated, user-friendly appearance but to provide the best service, it is necessary to evaluate the various problems that arise through review analysis. This study aims to conduct sentiment analysis of BCA Mobile application reviews taken from the Google Play Store, with data totaling 1,200 reviews scraping results using Google Collaboratory python programming language, to categorize negative and positive reviews used manual labeling for more accurate results, the Naïve Bayes approach is used in classifying positive and negative category reviews due to the ability of this algorithm to handle text data. However, the weakness of Naïve Bayes which is sensitive to irrelevant features can cause a decrease in accuracy. This research implements Genetic algorithm to improve the performance of Naïve Bayes. The results showed that the application of Genetic algorithm successfully increased the accuracy, precision of Naïve Bayes classification 95%, precision 92% to accuracy 98%, precision 99%, which proved the effectiveness of Genetic algorithm in optimizing the model and improving the quality of sentiment analysis.
Optimization of the K-Nearest Neighbors (KNN) Algorithm in Imbalanced Dataset Classification Using the SMOTE Technique Abi Fajar Ahmad Fauzi; Ahmad Faqih; Kaslani
Journal of Artificial Intelligence and Engineering Applications (JAIEA) Vol. 4 No. 2 (2025): February 2025
Publisher : Yayasan Kita Menulis

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.59934/jaiea.v4i2.756

Abstract

The naturalization of players for Indonesia's national football team has sparked diverse reactions on Twitter, ranging from support to opposition. This situation poses challenges for sentiment analysis, particularly in interpreting public opinion on the policy. A significant challenge arises from the imbalance in sentiment classes, with neutral sentiments outweighing positive and negative ones. This research investigates the effect of class imbalance on sentiment analysis accuracy by employing the KNN algorithm enhanced with the SMOTE technique. A quantitative approach is used, adopting an experimental method aligned with the KDD process stages. The findings reveal that the KNN algorithm without SMOTE achieved an accuracy of 54.77%, with a Precision of 0.65, Recall of 0.57, and F1-Score of 0.44. However, integrating SMOTE with the KNN algorithm significantly improved the outcomes, boosting accuracy to 81.49%, with a Precision of 0.87, Recall of 0.80, and F1-Score of 0.80. These results demonstrate that oversampling techniques like SMOTE are highly effective in mitigating class imbalance and enhancing classification performance, especially for underrepresented classes. This study underscores the efficacy of SMOTE as a solution for addressing class imbalance in sentiment analysis tasks.
Sales Data Classterization Analysis Using K-Means Method for Marketing Strategy Development Mifta Almaripat; Ahmad Faqih; Ade Rizki Rinaldy
Journal of Artificial Intelligence and Engineering Applications (JAIEA) Vol. 4 No. 2 (2025): February 2025
Publisher : Yayasan Kita Menulis

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.59934/jaiea.v4i2.792

Abstract

In the digital era, utilizing sales data is very important to support strategic decision making. This research aims to overcome the problems faced by 9Doors Store in optimizing marketing strategies and stock management. The main problem faced is the lack of in-depth analysis of existing sales data, which results in difficulties in formulating appropriate marketing strategies and efficient stock management. For this reason, this research applies the K-Means Clustering method to group products based on customer purchasing behavior characteristics. The data used includes product categories, selling prices, initial stock, number of products sold, and total sales obtained from 9Doors Store during the period March to September 2024. The method used in this research is Data Mining approach with K-Means algorithm, which is implemented using RapidMiner software. The data analysis process goes through Knowledge Discovery in Databases (KDD) stages, including data collection, data cleaning (preprocessing), data transformation, and data mining using K-Means. Cluster evaluation is done using Davies-Bouldin Index (DBI) to assess the quality of clustering results. The results of this study show that the division of sales data into three clusters provides optimal results with the lowest DBI value (0.106), which indicates efficient clustering. This finding identifies products with high, medium, and low sales levels, which can be used to formulate more targeted marketing strategies. With these results, Toko 9Doors can improve stock management and design more effective promotions based on better customer segmentation.
Implementation of the Naive Bayes Method in Sentiment Analysis of Spotify Application Reviews Agung Triyono; Ahmad Faqih; Fathurrohman
Journal of Artificial Intelligence and Engineering Applications (JAIEA) Vol. 4 No. 2 (2025): February 2025
Publisher : Yayasan Kita Menulis

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.59934/jaiea.v4i2.824

Abstract

This study focuses on sentiment analysis of Spotify application reviews on Google Play Store using the Naive Bayes algorithm. As a leading music streaming platform, Spotify receives diverse user feedback that reflects their experiences, complaints, and satisfaction. Sentiment analysis aids in understanding user opinions, enhancing services, and innovating features. The research involves collecting user reviews via web scraping, followed by preprocessing steps such as text cleaning, tokenization, normalization, stopword removal, and stemming. The Term Frequency-Inverse Document Frequency (TF-IDF) method is employed to assign weights to words, highlighting their significance in reviews. The Naive Bayes algorithm categorizes sentiments into positive, negative, and neutral classes. Performance evaluation uses a confusion matrix to measure accuracy, precision, recall, and F1-score. Results indicate that Naive Bayes effectively classifies large volumes of unstructured data with high accuracy and efficiency. This study contributes practically by offering actionable insights to improve Spotify's services and theoretically by advancing sentiment analysis methodologies using machine learning. The findings highlight the algorithm's potential to understand user needs and address issues, reinforcing its value in text analytics for mobile applications.
ASPEK MORALITAS PEMBELAJARAN SISWA SISWI MADRASAH ALIYAH MIFTAHUL ULUM LUMAJANG Mohammad Sholehuddin; Ahlam Musaydah; Ahmad Faqih
SIRAJUDDIN : Jurnal Penelitian dan Kajian Pendidikan Islam Vol 2 No 1 (2022): Sirajuddin Desember 2022
Publisher : P3M STAI MIFTAHUL ULUM LUMAJANG

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.55120/sirajuddin.v2i1.944

Abstract

Pendidikan merupakan hal penting dalam kehidupan di dunia, di tengahterpuruknya peradaban bangsa ini, gencarnya informasi, dan lepasnya sekat antarbangsa lewat teknologi informasi, peran guru kian strategis untuk mengambil salahsatu peran yang menopang pada tegaknya peradaban manusia Indonesia di waktuyang akan datang, Di Madrasah Aliyah Miftahul Ulum Lumajang adalah salah satumadrasah yang selalu mengambil langkah-langkah inovatif dalam mengembangkankualitas lembaga, hal ini dibuktikan dengan segudang prestasi yang diraih oleh parasiswa dan siswi dalam perlombaan dan prestasi yang diraih oleh lembaga.penelitian ini memakai metode penelitian deskriptif kualitatif karena dalampenelitian ini akan menggambarkan dan menginterpretasi objek penelitian sesuaidengan apa adanya. Penelitian ini akan mengunakan pendekatan kualitatif.Pendekatan kualitatif ini dipilih karena objek penelitian ini berupa proses ataupembentukan moral siswa. Data yang diperoleh dikumpulkan melalui wawancaradan pengamatan langsung dilapangan. Dalam strategi penerapan metode inkulkasinilai moral terhadap siswa-siswi Madrasah Aliyah Miftahul Ulum Lumajang,ditemukan ada beberapa cara, antara lain: melalui program pembiasaan, kegiatanintrakurikuler, kegiatan ekstrakurikuler, dan melalui keteladanan dari seorang gurukepada siswa-siswinya. Terkait dengan pelaksanaan 4 cara yang dilakukanmadrasah dalam penanaman nilai moral terhadap siswa sudah baik, akan tetapipada penerepan metode inkulkasi di dalam kelas, akan efektif bila diberlakukanbeberapa metode yang saling keterkaitan, lalu juga ditemukan fasilitas programekstrakurikuler masih ada yang kurang, jadi perlu dianggarkan dana untukmelengkapi fasilitas ekstrakurikuler yang masih kurang dan pemberian keteladananseorang guru dinilai masih perlu ditingkatkan lagi. Tentunya ini menjadi temuanbagi peneliti sebagai evaluasi madrasah untuk kedepannya
Edukasi Gizi Masyarakat Dan Cek Kesehatan Gratis Di Desa Jayalaksana Haqiyah, Aridhotul; Faqih, Ahmad
MADDANA Jurnal Pengabdian Kepada Masyarakat Vol 4 No 2 (2024): MADDANA: Jurnal Pengabdian Kepada Masyarakat
Publisher : Fakultas Keguruan dan Ilmu Pendidikan Universitas Islam 45 Bekasi

Show Abstract | Download Original | Original Source | Check in Google Scholar

Abstract

Random examination of uric acid levels in the people of Jayalaksana Village, Branchbungin District, Bekasi Regency, West Java aims to educate the public about Nutrition Education as an early detection of emerging diseases. The more people who know about uric acid levels, it is hoped that it can change the behavior of people's food intake and nutrition. So that disease prevention can be reduced, especially in people who have high risk factors. The educational method is carried out by counseling on nutrition and health checks on February 25, 2023 with community subjects, both men and women. The medical examination carried out is to measure the level of blood sugar levels. Based on the activities carried out, the results obtained were 25 respondents. A total of 15 were female and 10 were male. Found in residents who are female as much as 60% and male as much as 55% on blood sugar levels. So it can be concluded that through nutrition counseling and examination of uric acid levels the community can find out the blood sugar level which is used as a reference to regulate healthy and nutritious food intake patterns to reduce the risk of disease
Co-Authors -, Kaslani Abdullah Baharun Abi Fajar Ahmad Fauzi Achmad Supandi Ade Rizki Rinaldi Ade Rizki Rinaldy Adellia Putriani Adjie Setyadj, Mochammad Adnan Adnan Agung Triyono agus bahtiar Agus Riyadi Ahlam Musaydah Ahmad Jihadi Ahmad Rifai Akhmad Abu Khasan Muzakki Akhmad Subhan Al Ghozali, Muhammad Iqbal Ali, Ashehad Aswen Alma’as, Azis Amelia, Mita Andi Setiawan Andriawan, Dimas Annisa Rahmi Anshari, Rahman Ardiyanto Saleh Modjo Arif Rinaldi Dikananda Arlandy, Kevin Salsabil Arnas Arnas, Arnas Arum Sari Arya Wijaya, Arya Athhar Hafizha Luthfi ayu hardani, anita Aziz Ramadhani Badruddin Bafadal, Mentarry Bambang Siswoyo Basysyar, Fadhil Muhammad Chatarina Umbul Wahyuni Chulyatunni’mah Dadang Sudrajat Devika Rahayu Daud Dewi Wahyuni K. Baderan Diding Herudin Diding Herudin Diding Herudin, Diding Herudin Dienwati Nuris, Nisa Dikananda, Arif Rinaldi Dikananda, Fatihanursari Dini Andriyani Dita Rizki Amalia Dita, Mesya Sabhna Adma Djamadi, Dian Anggreini Dwi Kusuma, Lukman Edi Tohidi Eka Permana, Sandy Enjelita, Ratu Erieska Aprilyanti Esa Putra, Arga Ezra Pratama, Daffa Fadhil Muhammad Basysyar Fadilah, Euis Fajri, Ibnu Fajria, Azzahra Moudy Fathurrohman, Fathurrohman Febiyanto, Anggi Fidya Arie Pratama Fuad Pontoiyo Gagarin, Muhamad Yuri Giannetti, Niccolo Gifthera Dwilestari Gilang Ramadhan Gumelar, Restu Habiballoh, Hafshoh Hafshoh Habiballoh Hamdan, Faiz Dzul Fahmi Hamzah, Hasyrul Haqiyah, Aridhotul Hasim Hasim Hasim Hasim Herdiyana, Ruli Hermawan, Bagus Hermawan, Muhammad Andi Hidayat, Manarul Hikmah Maulani Himawan, Toni Iffah Adelia, Luthfiyyah Ikraeni Safitri Ilah Holilah Ilma’nun, M. Lulu Iqbal Syaidin, Fadli Jamiatur Rasyidah Jannah, Afni Nur Juliandro, Daniel Juramang, Risnayanti R K. Toiyo, Frandika Kadir, Rian Kaslani Khaerul Anam Khairul Akmal, Khairul Khairussalam Khoirul Huda, Muhammad Knohl, Alexander Komalasari, Cahyaningrum Kurniasih, Desta Dwi La Alio La Alio Laili Hidayatun Nikmah Laksono, Agung Lestari, Anjar Ayuning Lestari, Wien Lila Zulfa Kamila M. Basysyar, Fadhil Ma'rufah, Ummu Madyant Mahludin H. Baruwadi Maman Abdurrahman Manarul Hidayat Maulana Sidiq, Cecep Maulana, Haris Mey Yulan Moko Mia Nurmala Mifta Almaripat Miftahul Huda Mohamad Riad Solihin Mohammad Sholehuddin Mohammad Syaefudulloh Mubarok, Fatkhan Muh. Arfah Syam Muhammad Daffa Ayyasy Muhammad Fajid, Marfelio Muharram Muharram Muhfidz Hidayat, Aziz Muhibuddin Mukdin, Novita B Mukhyidin, Abdul Mulyana, Dani Mulyawan Mulyawan Mulyawan, Mulyawan Nalahuddin Saleh Narasati, Riri Narasati Nasruddin Nasruddin Nida Naswa Ningrum, Cistia Ningsih, Indah Ratna Nisa Sari, Ainun Norma Feti Farida Novi Mardiana Nur Atika Astriani Nur Farida, Farah Nur Halimah Nur Rochmah, Aulia Nuraini, Asyifa Nurhadiansyah, Nurhadiansyah Nurjana Adi Wijaya Nurul Aini, Yuli Odi Nurdiawan Oktavia, Riska Permadani Pertiwi, Pirda Parida Permana , Sandy Eka Permana, Sandy Eka Pratama, Denni Pratama, Fidya Arie R. Juramang, Risnayanti Rahayu, Helda Kusuma Rahma, Aliya Anisa Ramiro Firjatullah, Federicko Ramli Utina Raudya, Talitha Rifa'i, Ahmad Rifa’I, Ahmad Rinald, Ade Rizki Rinaldi Dikananda, Arif Riri Narasati Ristika Handarini Riyanto Adji Rizqy, Muhammad Enricco Rohmat, Cep Lukman Rosmeri Manurung, Agnes Rusmayana, Sigit Saeful Anwar, Saeful Saepu Qirom, Dani Saepudin, Asep Safitri, Ikraeni Sagita, Ayu Sandy Eka Permana Sandy Eka Permana, Faqih Selly Novita Sari Septianto, Muhamad Arif Sigit Rusmayana SM, Farid Solihudin, Dodi Subaegi, Angga Sugihartono, Tri Suharno, Achmad Sulaeman, Muhamad Supandi, Achmad Suryani Dewi, Ike Susana, Heliyanti Syaefudulloh, Mohammad Syam, Muh Arfah Syam, Muh. Arfah Syayid Al Manar Tania June Tati Suprapti Tengku Riza Zarzani N Tissa Aunilla Tomayahu, Tian Toriquddin Umar, Achmad Jauhari Wahyu Ningrum Sulistyowati Wanada, Gada Wanda, Aliffa Wijaya, Nurjana Adi Yonny Koesmaryono Yoshua, Deden Yudhistira Arie Wijaya Yuliantin, Yovi